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All Journal Jurnal Pekommas Al Tijarah Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Jurnal Surya Masyarakat JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS JURIKOM (Jurnal Riset Komputer) Ensiklopedia of Journal JUPE : Jurnal Pendidikan Mandala Abdimas Universal Jurnal Ilmu Pendidikan Islam Jurnal Surya Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) INDRA: Jurnal Pengabdian kepada Masyarakat JURNAL MUARA PENDIDIKAN Jurnal Ilmu Pendidikan dan Sains Islam Interdisipliner Jurnal Penelitian Ilmu Pendidikan Indonesia Journal of Community Empowerment Jurnal Social Society Prosiding Pengabdian Ekonomi dan Keuangan Syariah
Ilham, Ahmad
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Klasifikasi Kunyit dan Temulawak dengan VGG16 dan Fuzzy Tsukamoto Berbasis Android Setyawan, Muhammad Rizki; Bahari Putra, Fajar Rahardika; Ilham, Ahmad; Suseno, Dimas Adi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8696

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use
Detection of Curcuma and Turmeric Differences Utilizing Fuzzy Tsukamoto Android-Based CCN Model Putra, Fajar Rahardika Bahari; Setyawan, Muhammad Rizki; Ilham, Ahmad; Suseno, Dimas Adi
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2857.276-291

Abstract

Turmeric and curcuma are herbs that are often used in medicine and cooking. However, their similar shapes and colours make it difficult for people, especially in Southwest Papua, to distinguish between them directly. According to the Central Statistics Agency (BPS) in 2023, turmeric production reached 18,302 units, far higher than turmeric, which only reached 2,950 units. Based on field interviews in Southwest Papua, more than 60% of respondents had difficulty distinguishing turmeric from turmeric. To address this issue, this research develops an Android-based classification system by integrating the Fuzzy Tsukamoto algorithm with Convolutional Neural Network (CNN) models. Five CNN models VGG16, MobileNetV2, NASNetMobile, EfficientNetB2, and EfficientNetB3 were selected based on their balance between computational efficiency (MobileNetV2, NASNetMobile), depth and proven stability (VGG16), and modern scalable architectures (EfficientNetB2 and B3). Each model was combined with fuzzy logic to enhance classification accuracy. he dataset consisted of 800 images of curcuma and turmeric obtained from Kaggle and field collections. The data were divided into training, validation, and testing sets, and augmented through a series of transformations including rescaling to a range of 0 to 1, rotation up to 40 degrees, horizontal shift of 20%, angular distortion (shear) of 20%, zoom up to 30%, horizontal flipping, and brightness adjustment. Empty areas generated during augmentation were filled using the nearest pixel value with the ‘nearest’ mode to preserve image integrity. Training was performed using the AdamW optimizer and fine-tuning. Model evaluation employed accuracy, precision, recall, F1-score, and confusion matrix metrics. The results showed that the VGG16 model performed best, achieving 97% accuracy, 98% precision, 97% recall, and 98% F1-score, as confirmed by the classification report and confusion matrix. This model was also the most stable when tested on the Android system, while EfficientNetB2 and B3 produced less satisfactory outcomes. These findings demonstrate that combining CNN and Fuzzy Tsukamoto improves the classification accuracy of images with high visual similarity. The proposed system has the potential to be applied as a direct plant identification tool in the field and can be further extended to classify other visually similar plants
Inovasi Sosial Tubanan Agrocyrcleforestry: Sebuah Studi Menggunakan Metode Social Return On Investment (SROI) A. Khoirul Anam; Arifin, Miftah; Mahaputra, Wahyu; Ilham, Ahmad
Jurnal Nusantara Aplikasi Manajemen Bisnis Vol 8 No 2 (2023): Jurnal Nusantara Aplikasi Manajemen Bisnis
Publisher : UNIVERSITAS NUSANTARA PGRI KEDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/nusamba.v8i2.19903

Abstract

Research aim: The study aims to assess the value of the impact of PT PLN UIK TJB's CSR program on the implementation of social innovation in Tubanan agroforestry. In this approach, the effect of the program has an essential meaning for the beneficiaries of the program, namely the farming community group in Tubanan Village. Design/Methode/Approach: This study used social return on investment (SROI) as a research methodology. This research was conducted on the beneficiaries of the Tubanan agroforestry program and considered all stakeholders directly or indirectly involved in the program. The research informants numbered 20 people who were members of the LMDH Tunas Agung in Tubanan Village. Research Finding: The results showed that the CSR programs generated social benefits on investment and provided economic, social, and environmental benefits. SROI as a solution that changes the mindset of investment analysis based on outcomes is not just output. Theoretical contribution/Originality: This study allows us to expand the evidence of the critical role of social innovation for farming community groups, but so far, little has been studied about the application of SROI as an assessment methodology. Practitionel/Policy implication: The results of the SROI analysis become the basis for improving the planning of subsequent CSR programs. Research limitation: The selection of financial results and the proxies used are potentially biased, even though the proxies have been quantified over a potential range, the impact value has been reduced by filters (deadweigh, attribution, displacement, drop-off), and only emphasizes the impact on hard results rather than soft ones, which are considered less valuable.
Transformasi Lembaga Pendidikan Islam Menghadapi Era Society 5.0: Strategi Organizational Learning Untuk Peningkatan Kualitas Sumber Daya Guru PAI Ilham, Ahmad; Riadi, Dayun; Suradi, Suradi; Ruyasnyah, Muhammad Agustian; Adzim, Rahmad Hidayat Nur
Jurnal Penelitian Ilmu Pendidikan Indonesia Vol. 4 No. 4 (2025)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jpion.v4i4.853

Abstract

Penelitian ini bertujuan untuk mengeksplorasi praktik organizational learning di lembaga pendidikan Islam dalam meningkatkan kapabilitas guru Pendidikan Agama Islam (PAI) menghadapi tantangan pedagogi di era Society 5.0. Metode penelitian yang digunakan adalah kualitatif deskriptif dengan pengumpulan data melalui wawancara mendalam, observasi partisipatif, dan studi dokumentasi. Analisis data dilakukan secara tematik menggunakan kerangka Miles, Huberman, dan Saldaña (2019). Hasil penelitian menunjukkan bahwa penerapan pembelajaran organisasi di lembaga pendidikan Islam meliputi empat dimensi utama: (1) peningkatan kapabilitas guru melalui pelatihan berkelanjutan, (2) kolaborasi dan sharing knowledge antar guru, (3) pemanfaatan media digital dalam pembelajaran, dan (4) evaluasi berkelanjutan serta inovasi praktik pendidikan. Keempat dimensi ini membentuk ekosistem pembelajaran yang adaptif, inovatif, dan relevan dengan kebutuhan peserta didik, sekaligus selaras dengan nilai-nilai Islam. Temuan ini menegaskan bahwa lembaga pendidikan Islam yang konsisten menerapkan organizational learning mampu menghasilkan guru PAI yang profesional, kreatif, adaptif, dan mampu menghadapi dinamika pedagogi di era digital.
ANALISIS STRATEGI PEMASARAN PRODUK KESEHATAN POCARI SWEET UNTUK MENINGKATKAN PENJUALAN DI PT AMERTA INDAH OTSUKA PASURUAN Ulum Firmansyah, Adibul; Matnin, Matnin; Ilham, Ahmad; Umam, Hoirul; Tamam, Badrut; Andrian, Fiki
Prosiding Pengabdian Ekonomi dan Keuangan Syariah Vol. 4 No. 2 (2025): Prospeks
Publisher : LP2M IAI AL-KHAIRAT PAMEKASAN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pocari Sweat merupakan salah satu produk minuman isotonik terkemuka di Indonesia yang diproduksi oleh PT Amerta Indah Otsuka, termasuk di pabrik Pasuruan yang memiliki peran strategis dalam produksi dan distribusi. Persaingan industri minuman kesehatan yang semakin ketat menuntut perusahaan untuk menerapkan strategi pemasaran yang tepat agar mampu meningkatkan penjualan dan mempertahankan pangsa pasar. Penelitian ini bertujuan untuk menganalisis strategi pemasaran produk kesehatan Pocari Sweat dalam upaya meningkatkan penjualan di PT Amerta Indah Otsuka Pasuruan. Metode yang digunakan dalam penulisan ini adalah metode deskriptif kualitatif dengan pendekatan studi pustaka dan analisis konseptual terhadap strategi pemasaran perusahaan melalui pendekatan STP (Segmentasi, Targeting, dan Positioning), bauran pemasaran 4P (Product, Price, Place, Promotion), serta analisis SWOT. Hasil analisis menunjukkan bahwa Pocari Sweat memiliki keunggulan dari sisi kualitas produk, citra merek yang kuat, serta jaringan distribusi yang luas. Namun, tantangan tetap muncul berupa persaingan harga dan persepsi masyarakat yang masih menganggap minuman isotonik hanya untuk aktivitas olahraga. Strategi yang direkomendasikan untuk meningkatkan penjualan meliputi penguatan promosi berbasis edukasi kesehatan, optimalisasi digital marketing lokal, perluasan distribusi, serta penguatan kerja sama dengan komunitas dan institusi. Dengan penerapan strategi yang tepat dan berkelanjutan, PT Amerta Indah Otsuka Pasuruan memiliki peluang besar untuk meningkatkan penjualan Pocari Sweat secara signifikan.
Robust Few Shot Biological Pathology Classification via Optimized Contrastive MobileNetV2: A Transferable Model for Low Resource Medical Imaging Prawira, Nurul Adi; Firmansyah, Muhammad; Marutho, Dhendra; Ouhab, Achraf; Ilham, Ahmad
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.20179

Abstract

Artificial intelligence has revolutionized computational diagnostics, however deploying reliable intelligent systems in extreme low-resource environments remains a critical structural challenge in health informatics. Conventional deep learning architectures, such as standard Convolutional Neural Networks (CNNs), are inherently data-hungry, making them prone to severe overfitting and catastrophic generalization failures when applied to rare biological pathologies. To overcome this limitation, we propose an Optimized Contrastive MobileNetV2 architecture embedded within a Few-Shot Learning (FSL) framework. By mathematically modifying the latent space representation using a contrastive loss function, the proposed model learns discriminative metric distances rather than relying on massive raw feature memorization. To rigorously validate the algorithm, we utilize a highly constrained dataset comprising merely 120 biological pathogen samples as a cross-domain proxy testbed, accurately simulating the extreme visual complexity and data scarcity typical of rare medical diagnostic scenarios. Extensive episodic evaluations demonstrate that the proposed methodology significantly outperforms conventional baselines. Under a 10-shot learning paradigm, the contrastive architecture achieved a macro-averaged accuracy of 89.2% and an F1-Score of 89.3%, remaining statistically robust against stochastic variations (p < 0.001). Furthermore, the integration of depthwise separable convolutions restricts the model complexity to approximately 3.4 × 10^6 parameters. Crucially, empirical evaluations confirm that this framework occupies merely 13.5 MB of physical storage and achieves an ultra-low inference latency of 12.5 ms per image. Ultimately, this study establishes a highly transferable, computationally efficient algorithmic model ready for seamless integration into intelligent clinical decision support systems and remote edge-computing health architectures.
Co-Authors A. Khoirul Anam A. Octamaya Tenri Awaru Abdollahi, Jafar Abdul Nizar Adi Nugroho Adilla, Nia Adinullhaq, Juyus Muhammad Adzim, Rahmad Hidayat Nur Agatra, Denaya Ferrari Noval Ahmad Ahmad Farhan, Ahmad Ahyana, Afan Arga Aini, Isna Nur Akhmad Fathurohman Akhmad Fathurrohman Al Malik, M. Warisa Alfiana, Elsa Wahyu Amal Witonohadi Amylia, Aura Anam, A Khoirul Andi Aco Agus, Andi Aco Andrian, Fiki Anggana, Muhammad Wahyu April Liana, Dhewi Apriliah, Mifta Apriyanto, Riki Ardhani, Yevi Alviatul Ariyanto, Nova Badrut Tamam Bahari Putra, Fajar Rahardika Bayu Kristianto Cornella, Barisma Ami Dewi Citrawati Dhendra Marutho Disma, Amanda Fatma Putri Dwi Setia Anugrah, Muhamad Fadli Emelia Sari Erwin Budianto Estuhono, Estuhono Fadilatul Fajriyah, Rizqi Febrianto Febrianto, Febrianto Firmasyah, Teguh Habyba, Anik Nur Herlyana, Yuniar Iveline Anne Marie Kahar, Muhammad Syahrul Kahayani, Zahra Kamaruddin, Syamsu A Khatimah, Andi Weyana Nurul Khomsiana, Yeni Aqnes Khumairah, Tuffahati Sahna Khusna, Meisya Maulida Kindarto, Asdani Koli, Yulenni Bandora Kurnia, Janu Yogi Lorenza, Diana Lukman Assaffat Luqman Assaffat Mahaputra, Wahyu Maharani, Anisya Matnin Maulida, Nur Khilya Miftah Arifin Muhamad, Farezki Muhammad Firmansyah, Muhammad Muhammad Munsarif Muhammad Rizki Setyawan Muhammad Sam&#039;an Muhammad Taufiqurrahman, Muhammad Munsarif, Muhammad Muza'in, Muhammad Muzayyanah, Ulfatul Elsa Nabila, Shadrina Putri Najamuddin Najamuddin, Najamuddin Natalia, Devitri Ni'am, Falahun Novia, Syakila Ana Sajidah Putri Noviandi Noviandi, Noviandi Nur, Muhammad Adiv Anas Nurmantoro, Irvan Ouhab, Achraf Parwadi Moengin Prawira, Nurul Adi Putra, Fajar Rahardika Bahari Putri, Berliana Qori’nurrahman, Faqihana Ananda Ramadhani, Arfido Ramadhani, Rima Dias Ramea Astri, Tita Riadi, Dayun Riski Amaliah, Riski Rizki Jayanti, Dian Ruyasnyah, Muhammad Agustian Safuan Safuan Sam’an, Muhammad Sangadji, Zulkarnain Saputra, Irwansyah Saputra, Tegar Sasmita, Nanda Yulia Setia Iriyanto Setianama, Mamur Setyaningsih, Ayu Sholakhudin, Akhmad Sundari Sundari Suradi Suradi Suryana, Yunita Friscilia Suseno, Dimas Adi Sutarno Sutarno Syafitiri, Urzha Dian Syaifani, M. Amin Trianita, Nisa Adelia Ulfa, Helya Cholifatul Ulinuha, Mohammad Ulum Firmansyah, Adibul Umam, Hoirul Wulan Cahya Ningrum